require(plotly)
require(tidyverse)
require(ggridges)
require(cowplot)
require(RColorBrewer)
require(grid)
require(ggtext)
old <- theme_set(theme_bw(base_size = 16))

This is the second part of the analysis. In the first part (see 20231229-pool-qc-PHO5.Rmd), I did QC and exported the filtered dataset. Here, I will continue working with that dataset and answer our biological questions. This notebook is an updated copy of 20240103-PHO5-analysis.Rmd. The main update is to compare the new 4/5 break point chimeras to the old ones.

Goal

  • Analyze the full chimera set flow results for PHO5pr-mCherry reporter.
  • Develop an analysis pipeline to perform QC, correction (if needed) and plotting the results.
  • Compare the new 4/5 breakpoint chimeras to the old set.
Symbol Old New Comment
CCCCS 210 406
CCCSC 211 422
SCCCS 216 408 =332
CCSSC 218 420
CSCSC 223 423
SCSCS 224 414
SSSCS 229 415
SSSSC 233 407
SSCCS 235 409
SSCSC 239 424
CCSCS 240 413
CSSSC 241 410
CSSCS 250 412
SCCSC 251 425
SCSSC 252 421
CSCCS 253 411
CSScsC 254 419
CCCcsC 258 418
SSSscS 278 416
CSSscS 279 417

Data

Import the background subtracted data

dat0 <- read_tsv("../input/20231230-PHO5-bg-subtracted-data.tsv", col_types = "cccccdddddc")
print("Date of experiments and # of failed samples")
[1] "Date of experiments and # of failed samples"
with(dat0, table(date, flag))
       flag
date    fail pass
  02/08    0   88
  02/09    0   89
  02/10   85    0
  02/11    2   84
  02/16    3   86
  02/18    2   87
  02/19    2   87
  02/20    0   90
  02/21    0   90
  02/22    1   85
  02/23    1   87
  03/30    0   54
  03/31    0   54
  12/28    0  178
  12/29    1  178

Filter the data

dat <- filter(dat0, host != "PHO84", flag == "pass", date != "02/10") %>% 
  # based on previous QC, the following sample (both replicates) have high
  # variance - one biological replicate is highly expressed, while the other 
  # two have mNeon, but barely any RFP expression.
  mutate(flag = case_when(
    plasmid == "233" & host == "pho2∆" ~ "high.var", 
    plasmid == "423" & host == "PHO2" ~ "high.var", 
    .default = flag
  ))

Chimera makeup information

meta <- read_tsv("../input/20231228-chimera-Pho4-makeup.txt", 
                 comment = "#", col_types = "ccccc")

Summarize data

Here we would like calculate the ratio of RFP/GFP for each chimera (plasmid) across all replicates, including from different days. Note that the parameter of interest is a ratio, which can be estimated using either “means of ratios” or “ratios of means”. These are just two specific instances of a more general estimator, representing two choices of the weights. The “means of ratios” first calculates the ratios for each replicate within a plasmid, then average them. In this calculation, each replicate is given the weight of 1/n (equal). The “ratios of means” first sum up the GFP and RFP values separately across the replicates for each plasmid, then take the ratio between them. In this estimator, the weight for each replicate is x / sum(x), where x is the denominator in the ratio, i.e., GFP. In other words, this estimator will give more weights to the replicates where the chimera had a higher expression level.

Both estimators are known to be biased. We will ignore that for the moment. In terms of a choice between the two, it seems that there is no reason to give more weights to the experiments with a higher GFP signal. So, the “means of ratios” seems a more natural choice. However, we will calcultae both and dedice later.

A final question is how to calculate the variance of the ratio estimate. According to the survey package manual, an approximate estimator for the variance is

\[ r = \frac{\bar{y}}{\bar{x}}, \text{where}\ \bar{y}=\frac{1}{n}\sum_{i=1}^{n}y_i\ \text{and}\ \bar{x}=\frac{1}{n}\sum_{i=1}^{n}x_i\ \\ \hat{V}(r) = (1-\frac{n}{N})(\frac{1}{\bar{x}^2})\frac{s_r^2}{n}\ \text{where}\ s_r^2=\frac{1}{n-1}\sum_{i=1}^{n}(y_i-rx_i)^2 \]

Assuming that N>>n, we can ignore the first term in the variance estimator. The rest can be calculated from the data

datsum <- dat %>%
  filter(!is.na(plasmid)) %>% 
  group_by(plasmid, host) %>% 
  summarize(
     n = n(),
    mG = mean(BL1.H),
    mR = mean(YL2.H),
     A = mean(YL2.H/BL1.H),
     r = mR/mG,
    s2 = 1/(n-1)*sum((YL2.H - r*BL1.H)^2),
    vr = 1/(mG^2)*s2/n,
    se = sqrt(vr),
    .groups = "drop"
  ) %>% 
  select(-s2, -vr)# %>% 
  #pivot_wider(names_from = host, values_from = BL1.H:`nR/G`) %>% 
  #mutate(`pho2∆/PHO2` = `R/G_pho2∆`/`R/G_PHO2`,
  #       `n.pho2∆/PHO2` = `nR/G_pho2∆`/`nR/G_PHO2`)

For each chimera, we would also like to calculate three values:

  1. A in pho2∆: this is its base activity without Pho2
  2. A in PHO2: this is its full activity with Pho2
  3. A_PHO2 / A_pho2∆: this is the Pho2 enhancement of activity

We assign the chimeras into several groups, based on their A_PHO2 and A_PHO2/A_pho2∆

ximera <- datsum %>%
  mutate(host = fct_recode(host, "pho2" = "pho2∆")) %>% 
  pivot_wider(id_cols = plasmid, names_from = host, values_from = A, names_prefix = "A_") %>% 
  mutate(
    s_PHO2 = A_PHO2 / A_PHO2[plasmid == "194"],
    s_pho2 = A_pho2 / A_pho2[plasmid == "194"],
    boost = A_PHO2 / A_pho2,
    group = case_when(
      plasmid %in% c("188", "194") ~ "ref",
      s_PHO2 < 0.2                 ~ "n.f.",
      .default = "chimera"
    ),
    group = fct_relevel(group, "ref", "chimera", "n.f.")
  ) %>% 
  right_join(select(meta, plasmid, set, symbol, full), by = "plasmid") %>% 
  mutate(symbol = fct_reorder(symbol, s_PHO2, .desc = TRUE)) %>% 
  relocate(c(set, symbol, group), .after = plasmid)

Did the new break points rescue?

ximera %>% 
  filter(set %in% c("N", "A", "SA", "SN")) %>% 
  arrange(symbol) %>% 
  mutate(across(where(is.numeric), ~ round(.x, 2))) %>% 
  write_tsv("../output/20240103-compare-old-new-4-5-breakpoints.tsv")

Export the summarized data

write_tsv(ximera, file = "../output/20240103-PHO5pr-chimera-summarized.tsv")

For this analysis, include the set information in symbol

ximera1 <- mutate(ximera, symbol = paste(symbol, set, sep = "_"))

Data selection

Write a function that can select a subset of the chimeras given a set of rules

my_data_select <- function(pattern = NULL, Set = NULL){
  # change region 4 into a consistent format
  tmp <- ximera %>% 
    mutate(
      symbol = as.character(symbol),
      Symbol = ifelse(
        nchar(symbol) == 5,
        paste0(str_sub(symbol, 1, 3), 
               str_sub(symbol, 4, 4), 
               str_sub(symbol, 4, 4), 
               str_sub(symbol, 5, 5)),
        symbol
      )) %>% 
    select(plasmid, Symbol)
  # starting set
  if(length(Set) == 0)
    xim <- filter(tmp, !plasmid %in% refs)
  else
    xim <- filter(tmp, set %in% Set, !plasmid %in% refs)
  # compare to the pattern
  try(if(nchar(pattern) != 6) stop("Pattern must be a string with 6 characters"))
  symbols = xim$Symbol # extrac the symbols for testing
  include = nchar(symbols) > 0 # initialize the inclusion vector
  for(i in 1:6){
    p = substr(pattern, i, i)
    if(p != "X" & p != "x"){ # ignore X and x
      test = toupper(str_sub(symbols, i, i)) == toupper(p)
      include = include & test
    }
  }
  select <- cbind(xim, include)
  return(select$plasmid[select$include])
}
my_data_select_m <- function(patterns = NULL, Set = NULL){
  all_selected = c()
  try(if(length(patterns) == 0) stop("No patterns provided"))
  for(i in patterns)
    all_selected = c(all_selected, my_data_select(pattern = i))
  return(unique(all_selected))
}

Plotting functions

Set up common parameters for thresholding and plotting

# reference Pho4 plasmid ids
refs <- c("188", "194")
# colors
date.colors = c(brewer.pal(name="Dark2", n = 8), brewer.pal(name="Paired", n = 8))
host.colors = c("PHO2" = "gray30", "pho2∆" = "gray70")
point.colors = c("PHO2" = "forestgreen", "pho2∆" = "purple4")
# 

Data prep and transform

my_data_prep <- function(selection){
  # given a selection of chimera ID (plasmid), prepare a data frame for plotting
  # subset data
  tmp <- ximera1 %>% 
    filter(plasmid %in% c(refs, selection)) %>% 
    select(plasmid, symbol, group) %>% 
    inner_join(dat, by = "plasmid") %>% 
  return(tmp)
}

Plot RFP/GFP ratio and individual components

my_plot_ratio <- function(selection){
  tmp <- my_data_prep(selection)
  p <- tmp %>% 
    select(-c(FSC.H, nGFP, nRFP, flag)) %>% 
    mutate(`R/G` = YL2.H/BL1.H) %>% 
    pivot_longer(cols = c(BL1.H, YL2.H, `R/G`), 
                 names_to = "parameter", values_to = "value") %>% 
    mutate(parameter = factor(parameter, levels = c("R/G", "YL2.H", "BL1.H"),
                              labels = c("RFP/GFP", "PHO5pRFP", "Pho4-GFP"))) %>% 
    ggplot(aes(x = symbol, y = value, group = host)) + 
    stat_summary(aes(group = host), fun.data = "mean_cl_boot", geom = "errorbar",
                 position = position_dodge(0.5), width = 0.3) +
    geom_bar(aes(fill = host), width = 0.5, alpha = 0.8,
             stat = "summary", fun = "mean", position = position_dodge(0.5)) +
    geom_point(data = function(x) subset(x, !symbol %in% c("CCCCC", "SSSSS")),
               aes(group = host, color = host), size = 1, shape = 3, alpha = 0.9,
               position = position_jitterdodge(dodge.width = 0.5, jitter.width = 0.1)) +
    scale_color_manual(values = point.colors) +
    scale_fill_manual(values = host.colors) +
    facet_grid(parameter~group, scales = "free", space = "free_x") +
    theme_bw(base_size = 18) + background_grid(minor = "none") + 
    xlab("Pho4 chimera") +
    theme(axis.text.x = element_text(angle = 30, hjust = 1, family = "mono"),
          legend.position = "top",
          axis.title = element_blank())
  return(p) 
}

Plot chimera relative activity to ScPho4 w/ Pho2 and boost factor

my_plot_rel_act <- function(selection){
  # given a selection of chimera IDs, plot their functionality w/PHO2
  # relative to ScPho4, and their boost
  p <- filter(ximera, plasmid %in% c(refs, selection)) %>%
    ggplot(aes(x = symbol, y = s_PHO2)) +
    geom_col(width = 0.3, color = "black", fill = "gray80") + 
    geom_hline(yintercept = 1, linetype = 2, color = "gray30") +
    facet_grid(.~group, scales = "free_x", space = "free_x") + 
    scale_y_continuous(labels = scales::percent) +
    xlab("Pho4 chimera") + ylab("A<sub>PHO2</sub>, chimera/ScPho4") +
    ggtitle("Chimera function in <em>S. cerevisiae</em>") +
    theme_bw(base_size = 18) + background_grid(minor = "none") +
    theme(axis.text.x = element_text(angle = 30, hjust = 1, family = "mono"),
          #strip.text = element_blank(), 
          plot.title = element_markdown(hjust = 0.5),
          axis.title.y = element_markdown(),
          legend.position = "top")
  return(p)
}
my_plot_boost <- function(selection){
  # given a selection of chimera IDs, plot their functionality w/PHO2
  # relative to ScPho4, and their boost
  # dat
  tmp <- filter(ximera, plasmid %in% c(refs, selection)) %>% 
    mutate(perc_pho2 = A_pho2/A_PHO2) %>% 
    pivot_longer(cols = c(s_PHO2, boost, perc_pho2), 
                 names_to = "parameter", values_to = "ratio")
  # labeller
  par.explain <- c(
    s_PHO2 = "Rel. A<sub>PHO2</sub>",
    boost = "Boost",
    perc_pho2 = "%A<sub>pho2∆</sub>"
  )
  p <- ggplot(tmp, aes(x = symbol, y = ratio)) +
    geom_col(width = 0.3, color = "black", fill = "gray80") +
    geom_hline(yintercept = 1, linetype = 2, color = "gray30") +
    facet_grid(parameter~group, scales = "free", space = "free_x",
              labeller = labeller(parameter = par.explain)) +
    theme_bw(base_size = 18) +
    background_grid(minor = "none") +
    theme(axis.text.x = element_text(angle = 30, hjust = 1, family = "mono"),
          axis.title.x = element_blank(),
          strip.text.y = element_markdown(size = rel(0.9))
          #plot.title = element_text(hjust = 0.5)
    )
  return(p)
}

High variance samples

Summarize the background subtracted data by calculating the means and cv for each strain.

cv <- dat %>% 
  select(-nGFP, -nRFP) %>%
  pivot_longer(FSC.H:YL2.H, names_to = "parameter", values_to = "intensity") %>% 
  group_by(date, plasmid, host, parameter) %>% 
  summarize(
    n = n(),
    mean = mean(intensity),
    cv = sd(intensity)/mean(intensity),
    .groups = "drop"
  ) %>% 
  arrange(desc(cv))

Number of replicates left for each sample

expt <- dat %>% 
  filter(host %in% c("PHO2", "pho2∆"), !plasmid %in% c("188", "194")) %>% 
  group_by(date, plasmid, host) %>% 
  summarize(n = n(), .groups = "drop")

expt %>% 
  ggplot(aes(x = plasmid, y = n)) +
  geom_col(aes(fill = host)) + 
  facet_grid(date ~ .) +
  scale_fill_manual(values = c("PHO2" = "gray30", "pho2∆" = "gray70")) +
  theme_minimal() + background_grid(major = "none") + panel_border(size = 0.5) +
  scale_y_continuous(name = "Replicates", breaks = c(6)) + xlab(NULL) +
  theme(axis.text.x = element_text(angle = 90),
        strip.text.y = element_text(angle = 0),
        legend.position = "top")

Use the control strain (pH194 with PHO2) to identify and correct for systematic biases

control <- filter(dat, plasmid == "194", host == "PHO2") %>% 
  separate(well, into = c("row", "col"), sep = 1) %>% 
  droplevels()

Model for mNeon

gfp.model.0 <- lm(BL1.H ~ log10(events) + date + row*col, data = control)
step(gfp.model.0)
Start:  AIC=2047.53
BL1.H ~ log10(events) + date + row * col

                Df Sum of Sq      RSS    AIC
- row:col        6    117125  6384575 2039.1
- log10(events)  1       159  6267609 2045.5
<none>                        6267450 2047.5
- date          13   4256724 10524173 2121.0

Step:  AIC=2039.08
BL1.H ~ log10(events) + date + row + col

                Df Sum of Sq      RSS    AIC
- log10(events)  1        15  6384590 2037.1
- row            3    195156  6579731 2038.9
<none>                        6384575 2039.1
- col            2    625788  7010363 2053.0
- date          13   4242245 10626820 2110.9

Step:  AIC=2037.09
BL1.H ~ date + row + col

       Df Sum of Sq      RSS    AIC
- row   3    197679  6582269 2036.9
<none>               6384590 2037.1
- col   2    630432  7015023 2051.2
- date 13   5905463 12290054 2136.8

Step:  AIC=2036.94
BL1.H ~ date + col

       Df Sum of Sq      RSS    AIC
<none>               6582269 2036.9
- col   2    630432  7212702 2050.5
- date 13   5905463 12487733 2133.9

Call:
lm(formula = BL1.H ~ date + col, data = control)

Coefficients:
(Intercept)    date02/09    date02/11    date02/16    date02/18    date02/19    date02/20  
    1792.20      -209.67      -259.92      -351.46      -242.33      -319.04      -499.04  
  date02/21    date02/22    date02/23    date03/30    date03/31    date12/28    date12/29  
    -343.79      -233.12      -439.50      -533.58      -569.08      -634.44      -548.06  
       col5         col9  
     -72.77      -140.33  
gfp.model.1 <- lm(BL1.H ~ date + col, data = control)

Model for PHO5pr::RFP

rfp.model.0 <- lm(YL2.H ~ log10(events) + date + row*col, data = control)
step(rfp.model.0)
Start:  AIC=2986.52
YL2.H ~ log10(events) + date + row * col

                Df  Sum of Sq        RSS    AIC
- row:col        6   15521638  849284378 2978.1
- log10(events)  1    6696987  840459727 2986.1
<none>                         833762740 2986.5
- date          13 1538501384 2372264124 3161.3

Step:  AIC=2978.06
YL2.H ~ log10(events) + date + row + col

                Df  Sum of Sq        RSS    AIC
- log10(events)  1    5909230  855193608 2977.4
<none>                         849284378 2978.1
- col            2   39191418  888475796 2982.7
- row            3  182437263 1031721641 3009.4
- date          13 1539530911 2388815289 3150.6

Step:  AIC=2977.39
YL2.H ~ date + row + col

       Df  Sum of Sq        RSS    AIC
<none>                855193608 2977.4
- col   2   42651346  897844954 2982.7
- row   3  192060792 1047254400 3010.3
- date 13 2411146165 3266339773 3208.7

Call:
lm(formula = YL2.H ~ date + row + col, data = control)

Coefficients:
(Intercept)    date02/09    date02/11    date02/16    date02/18    date02/19    date02/20  
    31443.4      -6198.7      -4381.6      -5892.7      -2929.5      -6150.9      -4224.1  
  date02/21    date02/22    date02/23    date03/30    date03/31    date12/28    date12/29  
    -4476.0      -3672.5      -4728.8      -8207.8      -9870.6     -11549.9     -11786.6  
       rowC         rowE         rowG         col5         col9  
    -1849.2      -2119.1      -2659.6       -784.3      -1125.8  
rfp.model.1 <- lm(YL2.H ~ log10(events) + date + row + col, data = control)

there are more systematic shifts in the RFP, significant for row, col, date and also # of events however, I won’t be removing these effects yet, because I’ve found that RFP/GFP ratios are pretty consistent across days. In other words, the variation in GFP and RFP may be cancelled out.

Check for each plasmid how consistent are the measurements between days

tmp <- dat %>% 
  # remove one sample with only one valid day of experiment
  filter(!(plasmid == "218" & host == "PHO2"), !plasmid %in% c("188", "194", NA)) %>% 
  nest(data = c(date, BL1.H, YL2.H), .by = c(plasmid, host))

day.var.gfp <- tmp %>% 
  mutate(model = map(data, function(df) lm(BL1.H ~ date, data = df)),
         tidied = map(model, broom::tidy)) %>% 
  unnest(tidied) %>% 
  filter(term != "(Intercept)") %>% 
  mutate(p.adj = p.adjust(p.value, method = "BH")) %>% 
  select(-data, -model) %>% 
  filter(p.adj < 0.10) %>% 
  arrange(plasmid, host)

day.var.rfp <- tmp %>% 
  mutate(model = map(data, function(df) lm(YL2.H ~ date, data = df)),
         tidied = map(model, broom::tidy)) %>% 
  unnest(tidied) %>% 
  filter(term != "(Intercept)") %>% 
  mutate(p.adj = p.adjust(p.value, method = "BH")) %>% 
  select(-data, -model) %>% 
  filter(p.adj < 0.10) %>% 
  arrange(plasmid, host)
# extract ximera names
refs <- c("188","194")
# make a test set
day.var.gfp.list <- unique(day.var.gfp$plasmid)
day.var.rfp.list <- unique(day.var.rfp$plasmid)

High day-to-day GFP variance: 212, 222, 229, 251, 277, 301, 326, 328, 334 High day-to-day RFP variance: 212, 216, 239, 241

Plotting components for chimeras with high day-to-day variance in Pho4-mNeon

```r
p <- my_plot_ratio(c(refs,day.var.gfp.list)) + 
    geom_point(data = function(x) subset(x, !symbol %in% c(\CCCCC\, \SSSSS\)),
               aes(group = host, color = date), size = 1, shape = 3, alpha = 0.9,
               position = position_jitterdodge(dodge.width = 0.5, jitter.width = 0.1)) +
    scale_color_manual(values = date.colors, guide = \none\)

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Scale for colour is already present. Adding another scale for colour, which will replace the existing scale.




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```r
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p

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<img 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" />

<!-- rnb-plot-end -->

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<!-- rnb-text-begin -->

> Watch out for CSCscC, SCCsS, SCCsS

Plotting components for chimeras with high day-to-day variance in _PHO5pr_-mCherry

<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxucCA8LSBteV9wbG90X3JhdGlvKGMocmVmcyxkYXkudmFyLnJmcC5saXN0KSkgKyBcbiAgICBnZW9tX3BvaW50KGRhdGEgPSBmdW5jdGlvbih4KSBzdWJzZXQoeCwgIXN5bWJvbCAlaW4lIGMoXFxDQ0NDQ1xcLCBcXFNTU1NTXFwpKSxcbiAgICAgICAgICAgICAgIGFlcyhncm91cCA9IGhvc3QsIGNvbG9yID0gZGF0ZSksIHNpemUgPSAxLCBzaGFwZSA9IDMsIGFscGhhID0gMC45LFxuICAgICAgICAgICAgICAgcG9zaXRpb24gPSBwb3NpdGlvbl9kb2RnZSh3aWR0aCA9IDAuNSkpICtcbiAgICBzY2FsZV9jb2xvcl9tYW51YWwodmFsdWVzID0gZGF0ZS5jb2xvcnMsIGd1aWRlID0gXFxub25lXFwpXG5gYGBcbmBgYCJ9 -->

```r
```r
p <- my_plot_ratio(c(refs,day.var.rfp.list)) + 
    geom_point(data = function(x) subset(x, !symbol %in% c(\CCCCC\, \SSSSS\)),
               aes(group = host, color = date), size = 1, shape = 3, alpha = 0.9,
               position = position_dodge(width = 0.5)) +
    scale_color_manual(values = date.colors, guide = \none\)

<!-- rnb-source-end -->

<!-- rnb-output-begin eyJkYXRhIjoiU2NhbGUgZm9yIGNvbG91ciBpcyBhbHJlYWR5IHByZXNlbnQuXG5BZGRpbmcgYW5vdGhlciBzY2FsZSBmb3IgY29sb3VyLCB3aGljaCB3aWxsIHJlcGxhY2UgdGhlIGV4aXN0aW5nIHNjYWxlLlxuIn0= -->

Scale for colour is already present. Adding another scale for colour, which will replace the existing scale.




<!-- rnb-output-end -->

<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxucFxuYGBgXG5gYGAifQ== -->

```r
```r
p

<!-- rnb-source-end -->

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<img 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" />

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> most of the day-to-day variance are canceled out after RFP/GFP normalization

# All chimera, scatter plot
Design the plot


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```r
my_scatter_plot <- function(pattern){
  selection = my_data_select(pattern = pattern)
  scatter.colors = c("ScPho4" = "forestgreen", "CgPho4" = "blue3", "cyan2", "other" = "gray20")
  names(scatter.colors)[3] = pattern
  p <- ximera %>% 
    mutate(A_PHO2 = signif(A_PHO2, digits = 2),
           A_pho2 = signif(A_pho2, digits = 2),
           group = case_when(
             symbol == "CCCCC" ~ "CgPho4",
             symbol == "SSSSS" ~ "ScPho4",
             plasmid %in% selection ~ pattern,
             .default = "other"
           ),
           group = fct_relevel(group, names(scatter.colors))) %>% 
    ggplot(aes(x = A_PHO2, y = A_pho2, label = symbol)) + 
    geom_point(aes(color = group), size = 2.5) + 
    scale_color_manual("Pho4 type", values = scatter.colors) +
    geom_abline(slope = 1) +
    theme_gray(base_size = 14)
  return(p)
}

this function is the same as above, but is used to plot region 4 effects alone, and doesn’t take any input

my_scatter_plot_fix <- function(){
  # this function is the same as above, but is used to plot region 4
  # effects alone, and doesn't take any input
  s1 = my_data_select(pattern = "XXXCCX")
  s2 = my_data_select(pattern = "XXXSSX")
  scatter.colors = c("ScPho4" = "forestgreen", "CgPho4" = "blue3", 
                     "P2ID:Cg" = "cyan2", "P2ID:Sc" = "palegreen",
                     "P2ID:mixed" = "gray20")
  p <- ximera %>% 
    mutate(A_PHO2 = signif(A_PHO2, digits = 2),
           A_pho2 = signif(A_pho2, digits = 2),
           group = case_when(
             symbol == "CCCCC" ~ "CgPho4",
             symbol == "SSSSS" ~ "ScPho4",
             plasmid %in% s1 ~ "P2ID:Cg",
             plasmid %in% s2 ~ "P2ID:Sc",
             .default = "P2ID:mixed"
           ),
           group = fct_relevel(group, names(scatter.colors))) %>% 
    ggplot(aes(x = A_PHO2, y = A_pho2, label = symbol)) + 
    geom_abline(slope = 1) +
    geom_point(aes(color = group), size = 2.5) + 
    scale_color_manual(NULL, values = scatter.colors) +
    labs(x = bquote(A[PHO2]), y = bquote(A[pho2])) +
    theme_minimal(base_size = 16) +
    theme(legend.text = element_text(size = rel(0.75)))
  return(p)
}

this function is the same as my_scatter_plot_fix except that it plots all the chimeras without coloring them differently. for figure 5

my_scatter_plot_all <- function(){
  # this function is the same as my_scatter_plot_fix except that it plots all the chimeras
  # without coloring them differently. for figure 5
  s1 = my_data_select(pattern = "XXXCCX")
  s2 = my_data_select(pattern = "XXXSSX")
  scatter.colors = c("ScPho4" = "forestgreen", "CgPho4" = "blue3", 
                     "P2ID:Cg" = "gray20", "P2ID:Sc" = "gray20",
                     "P2ID:mixed" = "gray20")
  scatter.size = c("ScPho4" = 3.5, "CgPho4" = 3.5,
                   "P2ID:Cg" = 2.5, "P2ID:Sc" = 2.5, "P2ID:mixed" = 2.5)
  p <- ximera %>% 
    mutate(A_PHO2 = signif(A_PHO2, digits = 2),
           A_pho2 = signif(A_pho2, digits = 2),
           group = case_when(
             symbol == "CCCCC" ~ "CgPho4",
             symbol == "SSSSS" ~ "ScPho4",
             plasmid %in% s1 ~ "P2ID:Cg",
             plasmid %in% s2 ~ "P2ID:Sc",
             .default = "P2ID:mixed"
           ),
           group = fct_relevel(group, names(scatter.colors))) %>% 
    ggplot(aes(x = A_PHO2, y = A_pho2, label = symbol)) + 
    geom_abline(slope = 1) +
    geom_point(aes(color = group, size = group)) + 
    scale_color_manual(NULL, values = scatter.colors) +
    scale_size_manual(values = scatter.size, guide = "none") +
    labs(x = bquote(A[PHO2]), y = bquote(A[pho2])) +
    theme_cowplot() + panel_border(color = "gray30", size = 1.2) +
    theme(legend.text = element_text(size = rel(0.8)),
          legend.position = "none",
          axis.title = element_text(face = 2, size = rel(1.2)),
          axis.line = element_blank())

  return(p)
}

Plot all chimeras, for Fig. 5

p <- my_scatter_plot_all()
ggsave(filename = "../img/20241121-all-chimera-scatter.png",
       plot = p, width = 4.5, height = 4, dpi = 300)
ggplotly(p + labs(x = "A<sub>PHO2</sub>", y = "A<sub>pho2</sub>") +
           theme_gray(base_size = 16) +
           theme(legend.text = element_markdown()), 
         tooltip = c("label", "x", "y"))

ANOVA

```r
split <- c(1,1,1,1,1); names(split) <- paste0(\P\, 1:5)
tmp <- ximera %>% 
  filter(set == \M\, group != \n.f.\) %>% 
  separate_wider_position(symbol, split) %>% 
  mutate(across(P1:P5, ~factor(.x, levels = c(\S\, \C\))))
lm <- lm(A_pho2 ~ (P1+P2+P3+P4+P5), data = tmp)
summary(lm)

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The main effects were calculated by averaging over all chimeras with CgPho4 region at the respective position. I'd like to break them down by backgrounds. For example, for region 3, I'd like to see the pairwise comparisons between CCCSS and CCSSS, where only region 3 differs. The steps are

1. select the region to be compared. split the symbol into two parts - the genotype of the focal region and the rest
2. group by the second part (rest) and calculate the differential


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```r
```r
my_calc_region_effect <- function(region, variable){
  # this function takes the name of a variable of interest
  # x specifies the foreground region, which will be examined for its effect on
  # the variable of interest.
  # it then transforms the ximera data frame to preserve only the variable of
  # interest, pivots it wider after grouping by the background composition.
  
  # prepare the data by mutating the symbol column into fg and bg
  valid.var <- c(\A_PHO2\, \A_pho2\, \s_PHO2\, \spho2\, \boost\)
  if(!variable %in% valid.var)
    stop(paste0(\Please specify one of the valid variable names:\, 
                paste(valid.var, collapse = \
x = 5
p1 <- my_plot_region_effect_onevar(x, "A_PHO2")
p2 <- my_plot_region_effect_onevar(x, "A_pho2")
subplot(p1, p2, margin = 0.05) %>% 
  layout(title = paste("Region", x, "swap effect on A_PHO2 and A_pho2", sep = " "),
         xaxis = list(title = paste0("Region ", x, " from CgPho4")),
         yaxis = list(title = paste0("Region ", x, " from ScPho4")) )

Here, I’d like to take what I build above and create a new tibble, in which each row is a different background (makeup of the chimera except for the focal region). The value columns are:

  1. dA_PHO2 = A_PHO2_Cg - A_PHO2_Sc
  2. dA_pho2 = A_pho2_Cg - A_pho2_Sc
  3. A_PHO2_Sc = A_PHO2_Sc

The goal is to plot dA_PHO2 and dA_pho2 side-by-side for each background.

```r
my_plot_region_effect_twovar_line_par <- function(regions, highlight = \none\){
  # this function uses my_comp_region_effect to generate the data
  # and plot the difference in A_PHO2 and A_pho2 between the CgPho4 vs ScPho4
  # in the focal region
  dat <- map_dfr(regions, \(region) my_comp_region_effect(region), .id = \region\) %>% 
    pivot_longer(cols = c(dA_PHO2, dA_pho2), 
                 names_to = \host\, values_to = \diff\) %>% 
    mutate(host = fct_recode(host, `PHO2` = \dA_PHO2\, `pho2∆` = \dA_pho2\),
           host = fct_relevel(host, \PHO2\))
  # specify legend title
  legend.title = \\
  if(highlight != \none\ & !highlight %in% regions){
    hl = as.numeric(highlight)
    dat <- mutate(dat, grp = str_sub(bg, hl, hl) %>% toupper(),
                  grp = fct_recode(grp, CgPho4 = \C\, ScPho4 = \S\))
    legend.title = paste(\Region\, highlight, \from\, sep = \ \)
  }else{
    dat <- mutate(dat, grp = ifelse(NF, \no\, \yes\))
    legend.title = \Functional\
  }
  # specify arrow annotation
  arrow.x = 0.7
  arrow.y = (max(dat$diff) - min(dat$diff)) / 5 
  p <- dat %>% 
    ggplot(aes(x = host, y = diff, label = bg)) +
    geom_point(aes(color = grp), size = 2, alpha = 0.8,
               position = position_jitter(0.1)) + 
    geom_line(aes(group = bg), linewidth = 0.2, alpha = 0.8) +
    facet_grid(grp ~ region, labeller = labeller(
      grp = c(CgPho4 = \P2ID:CgPho4\, ScPho4 = \P2ID:ScPho4\),
      region = label_both
    )) +
    scale_color_manual(\P2ID:\, values = c(\orange\, \gray20\)) +
    ylab(\Region swap effect (Cg-Sc)\) +
    theme_bw(base_size = 18) + 
    theme(
      axis.title.x = element_blank(),
      axis.title.y = element_text(size = rel(0.9)),
      legend.text = element_text(size = rel(0.8)),
      legend.title = element_text(size = rel(0.9)),
    )
  return(p)
}
my_plot_region_effect_twovar_line_par(c(1,2,3), \4\)
ggsave(\../img/20231224-region-swap-effect-1to3-on-4.png\)

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```r
```r
my_plot_region_effect_twovar_side <- function(region){
  # this function uses my_comp_region_effect to generate the data
  # and plot the difference in A_PHO2 and A_pho2 between the CgPho4 vs ScPho4
  # in the focal region
  dat <- my_comp_region_effect(region) %>% 
    pivot_longer(cols = c(dA_PHO2, dA_pho2), 
                 names_to = \host\, values_to = \diff\) %>% 
    mutate(host = fct_recode(host, `PHO2` = \dA_PHO2\, `pho2∆` = \dA_pho2\),
           host = fct_relevel(host, \PHO2\))
  p <- dat %>% 
    ggplot(aes(x = bg, y = diff, group = host)) +
    geom_col(aes(fill = host), position = position_dodge(0.9)) +
    scale_fill_manual(values = host.colors) +
    ylab(\Region swap diff (Cg vs Sc)\) +
    theme_cowplot(font_size = 20) + 
    panel_border(color = \gray30\) +
    background_grid(major = \y\, minor = \none\) +
    theme(axis.text.x = element_text(angle = 45, hjust = 1, family = \courier\),
          axis.title.x = element_blank(),
          legend.position = \top\)
  return(p)
}

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```r
```r
my_upper_triangular_mat <- function(alt = \C\, var = \A_PHO2\){
  # given the alternative allele (C/S) and a variable of interest, e.g., A_PHO2,
  # output an upper triangular matrix containing the values from the variable 
  # of interest, with the row and col numbers based on the first and second
  # positions containing the alternative allele. If all positions contain the 
  # reference allele, the value is subtracted from all values in the matrix
  # when just one position is the alternative allele, the value in the diagonal
  # is set. when there are more than 2 regions containing the alternative allele
  # skip.
  out_mat <- matrix(NA, nrow = 5, ncol = 5)
  ref_val <- NA
  dat <- filter(ximera, set == \M\) %>% 
    mutate(S = as.character(symbol) %>% toupper())
  for(i in seq(1, nrow(dat))){
    symbol = dat[i, \S\]
    # determine which positions contain the alternative allele
    p = str_locate_all(symbol, alt)[[1]][,\start\]
    l = length(p)   # how many positions contain the alt allele
    v = dat[[var]][i] # retrieve the value of the variable
    if(l == 0)
      ref_val = v
    else if(l == 1)
      out_mat[p, p] = v
    else if(l == 2)
      out_mat[p[1], p[2]] = v
  }
  out_mat = out_mat - ref_val
  return(out_mat)
}

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```r
```r
my_combined_triangular_mat <- function(alt = \C\){
  # given the alternative allele (C/S), output a matrix containing the values
  # for both with and without Pho2, arranged in two complementary triagular
  # matrices, with the row and col numbers based on the first and second
  # positions containing the alternative allele. If all positions contain the 
  # reference allele, the value is subtracted from all values in the matrix
  # when just one position is the alternative allele, the value in the diagonal
  # is set. when there are more than 2 regions containing the alternative allele
  # skip.
  out_mat <- matrix(NA, nrow = 6, ncol = 6)
  upper <- cbind(NA, my_upper_triangular_mat(alt, var = \A_PHO2\)) %>% 
    rbind(., NA)
  lower <- rbind(NA, t(my_upper_triangular_mat(alt, var = \A_pho2\))) %>% 
    cbind(., NA)
  out_mat = ifelse(is.na(upper), lower, upper)
  return(out_mat)
}

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# triangle heatmap
First, write a function to generate the data for plotting. If we are going to use ggplot, we need a tibble to store the data, something in the following form

| plasmid | symbol | RegionA | RegionB | A_PHO2 | A_pho2 | s_PHO2 | boost | perc_pho2 |
|:------- |:------ |:------- |:------- |:------ |:------ |:------ |:----- |:--------- |
| 209     | CCSCC  | 3       | 3       | 8.25   | 7.82   | 0.468  | 1.06  | 0.94      |

If we are ok with using non ggplot - heatmaps are not ggplot's strength anyways - we can just build a matrix.

Note that this way of summarizing the data has many limitaitons: 1) it requires specifying the reference, either CCCCC or SSSSS. Everything is measured against that; 2) it only shows pairwise (two region) interactions. This turns out to be fine with five regions, since every chimera can be expressed as either a 0, 1 or 2 region swap from one of the two reference genotypes. With 6 or more regions, higher level (3 or more region) interactions cannot be visualized this way. Because of this, we will focus on just the main set for this analysis.

To build the matrix, we need to first identify the chimeras that belong to the set. For that, we will use the "main" set, with the five region split, for the moment at least. The function will first determine which reference to use. If we use SSSSS as the reference, for example, we will assign 0 to the reference. All other chimeras with 1 or 2 regions from Cg will be used to fill an upper triangular matrix, using one of the values of interest, e.g., A_PHO2.

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```r
```r
my_plot_triangle_heatmap <- function(alt, var){
  # this function takes the output of the function above and makes a heatmap
  # using pheatmap function, then rotates it using grid graphics
  # thanks to https://bookdown.org/rdpeng/RProgDA/the-grid-package.html#grid-graphics-coordinate-systems
  # adding title based on https://davetang.github.io/muse/pheatmap.html
  
  # construct title of plot
  ref = ifelse(alt == \C\, \ScPho4\, \CgPho4\)
  bg = ifelse(var == \A_PHO2\, \with PHO2\, \w/o pho2\)
  my_title <- paste(\Epistasis between regions on\, ref, \background\, bg)
  test <- my_upper_triangular_mat(alt = alt, var = var)
  paletteLength = 50
  myColors <- colorRampPalette(c(\steelblue4\, \gray90\, \red\))(paletteLength)
  rng <- max(abs(test), na.rm = TRUE)
  myBreaks <- c(seq(-rng, 0, length.out=ceiling(paletteLength/2) + 1), 
                seq(rng/paletteLength, rng,
                    length.out=floor(paletteLength/2)))
  p <- pheatmap::pheatmap(test, color = myColors, breaks = myBreaks,
                          border_color = NA, na_col = NA, silent = TRUE,
                          cluster_cols = FALSE, cluster_rows = FALSE)
  vp <- viewport(x = 0.5, y = 0.25,
                 width = unit(4.5, \in\), height = unit(4.5, \in\), angle = 47) 
  grid.newpage()
  pushViewport(vp)
  grid.draw(p$gtable)
  popViewport()
  grid.text(label = my_title, x = 0.5, y = 0.95, gp = gpar(fontsize = 16, fontface = \bold\))
  return(p)
}

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```r
```r
my_plot_combined_triangle_heatmap <- function(alt){
  # this function takes the output of the function my_combined_triangular_mat()
  # using pheatmap function, then rotates it using grid graphics
  # thanks to https://bookdown.org/rdpeng/RProgDA/the-grid-package.html#grid-graphics-coordinate-systems
  # adding title based on https://davetang.github.io/muse/pheatmap.html
  
  # construct title of plot
  ref = ifelse(alt == \C\, \ScPho4\, \CgPho4\)
  my_title <- paste(\Epistasis between regions on\, ref, \background\)
  test <- my_combined_triangular_mat(alt = alt)
  paletteLength = 50
  myColors <- colorRampPalette(c(\steelblue4\, \gray90\, \red\))(paletteLength)
  rng <- max(abs(test), na.rm = TRUE)
  myBreaks <- c(seq(-rng, 0, length.out=ceiling(paletteLength/2) + 1), 
                seq(rng/paletteLength, rng,
                    length.out=floor(paletteLength/2)))
  p <- pheatmap::pheatmap(test, color = myColors, breaks = myBreaks,
                          border_color = NA, na_col = NA, silent = TRUE,
                          cluster_cols = FALSE, cluster_rows = FALSE)
  vp <- viewport(x = 0.5, y = 0.45,
                 width = unit(3, \in\), height = unit(2.8, \in\), angle = 47) 
  grid.newpage()
  pushViewport(vp)
  grid.draw(p$gtable)
  popViewport()
  grid.text(label = my_title, x = 0.5, y = 0.95, 
            gp = gpar(fontsize = 16, fontface = \bold\))
  grid.text(label = \With Pho2\, x = 0.1, y = 0.65, just = c(\left\, \top\),
            gp = gpar(fontsize = 14, fontface = \bold\))
  grid.text(label = \Without pho2\, x = 0.1, y = 0.25, just = c(\left\, \top\), 
            gp = gpar(fontsize = 14, fontface = \bold\))
  return(p)
}

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```r
```r
p1 <- my_plot_combined_triangle_heatmap(\C\)
p2 <- my_plot_combined_triangle_heatmap(\S\)

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```r
```r
p1 <- my_plot_triangle_heatmap(\C\, \A_PHO2\)
p2 <- my_plot_triangle_heatmap(\C\, \A_pho2\)
p3 <- my_plot_triangle_heatmap(\S\, \A_PHO2\)
p4 <- my_plot_triangle_heatmap(\S\, \A_pho2\)

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```r
p1 <- my_plot_combined_triangle_heatmap("C")
p2 <- my_plot_combined_triangle_heatmap("S")
p1 <- my_plot_triangle_heatmap("C", "A_PHO2")
p2 <- my_plot_triangle_heatmap("C", "A_pho2")
p3 <- my_plot_triangle_heatmap("S", "A_PHO2")
p4 <- my_plot_triangle_heatmap("S", "A_pho2")
---
title: "E013 Pho4 chimera activity analysis using PHO5 reporter, analysis"
author: "Bin He"
date: "2024-01-03 updated `r Sys.Date()`"
output:
  html_notebook:
    toc: yes
    toc_float: yes
    code_folding: hide
  pdf_document:
    toc: yes
  html_document:
    toc: yes
    df_print: paged
---

```{r message=FALSE}
require(plotly)
require(tidyverse)
require(ggridges)
require(cowplot)
require(RColorBrewer)
require(grid)
require(ggtext)
```

```{r}
old <- theme_set(theme_bw(base_size = 16))
```

This is the second part of the analysis. In the first part (see `20231229-pool-qc-PHO5.Rmd`), I did QC and exported the filtered dataset. Here, I will continue working with that dataset and answer our biological questions. This notebook is an updated copy of `20240103-PHO5-analysis.Rmd`. The main update is to compare the new 4/5 break point chimeras to the old ones.

# Goal
- Analyze the full chimera set flow results for _PHO5pr_-mCherry reporter.
- Develop an analysis pipeline to perform QC, correction (if needed) and plotting the results.
- Compare the new 4/5 breakpoint chimeras to the old set.

| Symbol | Old | New | Comment |
|:-------|:----|:----|:--------|
| CCCCS | 210 | 406 ||
| CCCSC | 211 | 422 ||
| SCCCS | 216 | 408 | =332 |
| CCSSC | 218 | 420 ||
| CSCSC | 223 | 423 ||
| SCSCS | 224 | 414 ||
| SSSCS | 229 | 415 ||
| SSSSC | 233 | 407 ||
| SSCCS | 235 | 409 ||
| SSCSC | 239 | 424 ||
| CCSCS | 240 | 413 ||
| CSSSC | 241 | 410 ||
| CSSCS | 250 | 412 ||
| SCCSC | 251 | 425 ||
| SCSSC | 252 | 421 ||
| CSCCS | 253 | 411 ||
| | | |
| CSScsC | 254 | 419 ||
| CCCcsC | 258 | 418 ||
| SSSscS | 278 | 416 ||
| CSSscS | 279 | 417 ||

# Data
Import the background subtracted data
```{r}
dat0 <- read_tsv("../input/20231230-PHO5-bg-subtracted-data.tsv", col_types = "cccccdddddc")
print("Date of experiments and # of failed samples")
with(dat0, table(date, flag))
```

Filter the data
```{r}
dat <- filter(dat0, host != "PHO84", flag == "pass", date != "02/10") %>% 
  # based on previous QC, the following sample (both replicates) have high
  # variance - one biological replicate is highly expressed, while the other 
  # two have mNeon, but barely any RFP expression.
  mutate(flag = case_when(
    plasmid == "233" & host == "pho2∆" ~ "high.var", 
    plasmid == "423" & host == "PHO2" ~ "high.var", 
    .default = flag
  ))
```

Chimera makeup information
```{r}
meta <- read_tsv("../input/20231228-chimera-Pho4-makeup.txt", 
                 comment = "#", col_types = "ccccc")
```

## Summarize data

Here we would like calculate the ratio of RFP/GFP for each chimera (plasmid) across all replicates, including from different days. Note that the parameter of interest is a ratio, which can be estimated using either "means of ratios" or "ratios of means". These are just two specific instances of a more general estimator, representing two choices of the weights. The "means of ratios" first calculates the ratios for each replicate within a plasmid, then average them. In this calculation, each replicate is given the weight of 1/n (equal). The "ratios of means" first sum up the GFP and RFP values separately across the replicates for each plasmid, then take the ratio between them. In this estimator, the weight for each replicate is x / sum(x), where x is the denominator in the ratio, i.e., GFP. In other words, this estimator will give more weights to the replicates where the chimera had a higher expression level.

Both estimators are known to be biased. We will ignore that for the moment. In terms of a choice between the two, it seems that there is no reason to give more weights to the experiments with a higher GFP signal. So, the "means of ratios" seems a more natural choice. However, we will calcultae both and dedice later.


A final question is how to calculate the variance of the ratio estimate. According to the `survey` package [manual](https://rstudio-pubs-static.s3.amazonaws.com/178965_fb60a0f7bbb44a6ea219713fb1a89a22.html), an approximate estimator for the variance is

$$
r = \frac{\bar{y}}{\bar{x}}, \text{where}\ \bar{y}=\frac{1}{n}\sum_{i=1}^{n}y_i\ \text{and}\ \bar{x}=\frac{1}{n}\sum_{i=1}^{n}x_i\ \\
\hat{V}(r) = (1-\frac{n}{N})(\frac{1}{\bar{x}^2})\frac{s_r^2}{n}\ \text{where}\ s_r^2=\frac{1}{n-1}\sum_{i=1}^{n}(y_i-rx_i)^2
$$

Assuming that N>>n, we can ignore the first term in the variance estimator. The rest can be calculated from the data

```{r}
datsum <- dat %>%
  filter(!is.na(plasmid)) %>% 
  group_by(plasmid, host) %>% 
  summarize(
     n = n(),
    mG = mean(BL1.H),
    mR = mean(YL2.H),
     A = mean(YL2.H/BL1.H),
     r = mR/mG,
    s2 = 1/(n-1)*sum((YL2.H - r*BL1.H)^2),
    vr = 1/(mG^2)*s2/n,
    se = sqrt(vr),
    .groups = "drop"
  ) %>% 
  select(-s2, -vr)# %>% 
  #pivot_wider(names_from = host, values_from = BL1.H:`nR/G`) %>% 
  #mutate(`pho2∆/PHO2` = `R/G_pho2∆`/`R/G_PHO2`,
  #       `n.pho2∆/PHO2` = `nR/G_pho2∆`/`nR/G_PHO2`)
```

For each chimera, we would also like to calculate **three values**:

1. A in _pho2∆_: this is its base activity without Pho2
1. A in _PHO2_: this is its full activity with Pho2
1. A_PHO2 / A_pho2∆: this is the Pho2 enhancement of activity

We assign the chimeras into several groups, based on their A_PHO2 and A_PHO2/A_pho2∆

```{r}
ximera <- datsum %>%
  mutate(host = fct_recode(host, "pho2" = "pho2∆")) %>% 
  pivot_wider(id_cols = plasmid, names_from = host, values_from = A, names_prefix = "A_") %>% 
  mutate(
    s_PHO2 = A_PHO2 / A_PHO2[plasmid == "194"],
    s_pho2 = A_pho2 / A_pho2[plasmid == "194"],
    boost = A_PHO2 / A_pho2,
    group = case_when(
      plasmid %in% c("188", "194") ~ "ref",
      s_PHO2 < 0.2                 ~ "n.f.",
      .default = "chimera"
    ),
    group = fct_relevel(group, "ref", "chimera", "n.f.")
  ) %>% 
  right_join(select(meta, plasmid, set, symbol, full), by = "plasmid") %>% 
  mutate(symbol = fct_reorder(symbol, s_PHO2, .desc = TRUE)) %>% 
  relocate(c(set, symbol, group), .after = plasmid)
```

Did the new break points rescue?
```{r}
ximera %>% 
  filter(set %in% c("N", "A", "SA", "SN")) %>% 
  arrange(symbol) %>% 
  mutate(across(where(is.numeric), ~ round(.x, 2))) %>% 
  write_tsv("../output/20240103-compare-old-new-4-5-breakpoints.tsv")
```

Export the summarized data
```{r}
write_tsv(ximera, file = "../output/20240103-PHO5pr-chimera-summarized.tsv")
```

For this analysis, include the set information in symbol
```{r}
ximera1 <- mutate(ximera, symbol = paste(symbol, set, sep = "_"))
```

## Data selection
Write a function that can select a subset of the chimeras given a set of rules
```{r}
my_data_select <- function(pattern = NULL, Set = NULL){
  # change region 4 into a consistent format
  tmp <- ximera %>% 
    mutate(
      symbol = as.character(symbol),
      Symbol = ifelse(
        nchar(symbol) == 5,
        paste0(str_sub(symbol, 1, 3), 
               str_sub(symbol, 4, 4), 
               str_sub(symbol, 4, 4), 
               str_sub(symbol, 5, 5)),
        symbol
      )) %>% 
    select(plasmid, Symbol)
  # starting set
  if(length(Set) == 0)
    xim <- filter(tmp, !plasmid %in% refs)
  else
    xim <- filter(tmp, set %in% Set, !plasmid %in% refs)
  # compare to the pattern
  try(if(nchar(pattern) != 6) stop("Pattern must be a string with 6 characters"))
  symbols = xim$Symbol # extrac the symbols for testing
  include = nchar(symbols) > 0 # initialize the inclusion vector
  for(i in 1:6){
    p = substr(pattern, i, i)
    if(p != "X" & p != "x"){ # ignore X and x
      test = toupper(str_sub(symbols, i, i)) == toupper(p)
      include = include & test
    }
  }
  select <- cbind(xim, include)
  return(select$plasmid[select$include])
}
my_data_select_m <- function(patterns = NULL, Set = NULL){
  all_selected = c()
  try(if(length(patterns) == 0) stop("No patterns provided"))
  for(i in patterns)
    all_selected = c(all_selected, my_data_select(pattern = i))
  return(unique(all_selected))
}
```

# Plotting functions
Set up common parameters for thresholding and plotting
```{r}
# reference Pho4 plasmid ids
refs <- c("188", "194")
# colors
date.colors = c(brewer.pal(name="Dark2", n = 8), brewer.pal(name="Paired", n = 8))
host.colors = c("PHO2" = "gray30", "pho2∆" = "gray70")
point.colors = c("PHO2" = "forestgreen", "pho2∆" = "purple4")
# 
```

Data prep and transform
```{r}
my_data_prep <- function(selection){
  # given a selection of chimera ID (plasmid), prepare a data frame for plotting
  # subset data
  tmp <- ximera1 %>% 
    filter(plasmid %in% c(refs, selection)) %>% 
    select(plasmid, symbol, group) %>% 
    inner_join(dat, by = "plasmid") %>% 
  return(tmp)
}
```

Plot RFP/GFP ratio and individual components
```{r}
my_plot_ratio <- function(selection){
  tmp <- my_data_prep(selection)
  p <- tmp %>% 
    select(-c(FSC.H, nGFP, nRFP, flag)) %>% 
    mutate(`R/G` = YL2.H/BL1.H) %>% 
    pivot_longer(cols = c(BL1.H, YL2.H, `R/G`), 
                 names_to = "parameter", values_to = "value") %>% 
    mutate(parameter = factor(parameter, levels = c("R/G", "YL2.H", "BL1.H"),
                              labels = c("RFP/GFP", "PHO5pRFP", "Pho4-GFP"))) %>% 
    ggplot(aes(x = symbol, y = value, group = host)) + 
    stat_summary(aes(group = host), fun.data = "mean_cl_boot", geom = "errorbar",
                 position = position_dodge(0.5), width = 0.3) +
    geom_bar(aes(fill = host), width = 0.5, alpha = 0.8,
             stat = "summary", fun = "mean", position = position_dodge(0.5)) +
    geom_point(data = function(x) subset(x, !symbol %in% c("CCCCC", "SSSSS")),
               aes(group = host, color = host), size = 1, shape = 3, alpha = 0.9,
               position = position_jitterdodge(dodge.width = 0.5, jitter.width = 0.1)) +
    scale_color_manual(values = point.colors) +
    scale_fill_manual(values = host.colors) +
    facet_grid(parameter~group, scales = "free", space = "free_x") +
    theme_bw(base_size = 18) + background_grid(minor = "none") + 
    xlab("Pho4 chimera") +
    theme(axis.text.x = element_text(angle = 30, hjust = 1, family = "mono"),
          legend.position = "top",
          axis.title = element_blank())
  return(p) 
}
```

Plot chimera relative activity to ScPho4 w/ Pho2 and boost factor
```{r}
my_plot_rel_act <- function(selection){
  # given a selection of chimera IDs, plot their functionality w/PHO2
  # relative to ScPho4, and their boost
  p <- filter(ximera, plasmid %in% c(refs, selection)) %>%
    ggplot(aes(x = symbol, y = s_PHO2)) +
    geom_col(width = 0.3, color = "black", fill = "gray80") + 
    geom_hline(yintercept = 1, linetype = 2, color = "gray30") +
    facet_grid(.~group, scales = "free_x", space = "free_x") + 
    scale_y_continuous(labels = scales::percent) +
    xlab("Pho4 chimera") + ylab("A<sub>PHO2</sub>, chimera/ScPho4") +
    ggtitle("Chimera function in <em>S. cerevisiae</em>") +
    theme_bw(base_size = 18) + background_grid(minor = "none") +
    theme(axis.text.x = element_text(angle = 30, hjust = 1, family = "mono"),
          #strip.text = element_blank(), 
          plot.title = element_markdown(hjust = 0.5),
          axis.title.y = element_markdown(),
          legend.position = "top")
  return(p)
}
```

```{r}
my_plot_boost <- function(selection){
  # given a selection of chimera IDs, plot their functionality w/PHO2
  # relative to ScPho4, and their boost
  # dat
  tmp <- filter(ximera, plasmid %in% c(refs, selection)) %>% 
    mutate(perc_pho2 = A_pho2/A_PHO2) %>% 
    pivot_longer(cols = c(s_PHO2, boost, perc_pho2), 
                 names_to = "parameter", values_to = "ratio")
  # labeller
  par.explain <- c(
    s_PHO2 = "Rel. A<sub>PHO2</sub>",
    boost = "Boost",
    perc_pho2 = "%A<sub>pho2∆</sub>"
  )
  p <- ggplot(tmp, aes(x = symbol, y = ratio)) +
    geom_col(width = 0.3, color = "black", fill = "gray80") +
    geom_hline(yintercept = 1, linetype = 2, color = "gray30") +
    facet_grid(parameter~group, scales = "free", space = "free_x",
              labeller = labeller(parameter = par.explain)) +
    theme_bw(base_size = 18) +
    background_grid(minor = "none") +
    theme(axis.text.x = element_text(angle = 30, hjust = 1, family = "mono"),
          axis.title.x = element_blank(),
          strip.text.y = element_markdown(size = rel(0.9))
          #plot.title = element_text(hjust = 0.5)
    )
  return(p)
}
```

# High variance samples

Summarize the background subtracted data by calculating the means and cv for each strain.
```{r}
cv <- dat %>% 
  select(-nGFP, -nRFP) %>%
  pivot_longer(FSC.H:YL2.H, names_to = "parameter", values_to = "intensity") %>% 
  group_by(date, plasmid, host, parameter) %>% 
  summarize(
    n = n(),
    mean = mean(intensity),
    cv = sd(intensity)/mean(intensity),
    .groups = "drop"
  ) %>% 
  arrange(desc(cv))
```

Number of replicates left for each sample
```{r}
expt <- dat %>% 
  filter(host %in% c("PHO2", "pho2∆"), !plasmid %in% c("188", "194")) %>% 
  group_by(date, plasmid, host) %>% 
  summarize(n = n(), .groups = "drop")

expt %>% 
  ggplot(aes(x = plasmid, y = n)) +
  geom_col(aes(fill = host)) + 
  facet_grid(date ~ .) +
  scale_fill_manual(values = c("PHO2" = "gray30", "pho2∆" = "gray70")) +
  theme_minimal() + background_grid(major = "none") + panel_border(size = 0.5) +
  scale_y_continuous(name = "Replicates", breaks = c(6)) + xlab(NULL) +
  theme(axis.text.x = element_text(angle = 90),
        strip.text.y = element_text(angle = 0),
        legend.position = "top")
```
Use the control strain (pH194 with PHO2) to identify and correct for systematic biases
```{r}
control <- filter(dat, plasmid == "194", host == "PHO2") %>% 
  separate(well, into = c("row", "col"), sep = 1) %>% 
  droplevels()
```

Model for mNeon
```{r}
gfp.model.0 <- lm(BL1.H ~ log10(events) + date + row*col, data = control)
step(gfp.model.0)
gfp.model.1 <- lm(BL1.H ~ date + col, data = control)
```

Model for PHO5pr::RFP
```{r}
rfp.model.0 <- lm(YL2.H ~ log10(events) + date + row*col, data = control)
step(rfp.model.0)
rfp.model.1 <- lm(YL2.H ~ log10(events) + date + row + col, data = control)
```
> there are more systematic shifts in the RFP, significant for row, col, date and also # of events
> however, I won't be removing these effects yet, because I've found that RFP/GFP ratios are pretty consistent across days. In other words, the variation in GFP and RFP may be cancelled out.

Check for each plasmid how consistent are the measurements between days
```{r}
tmp <- dat %>% 
  # remove one sample with only one valid day of experiment
  filter(!(plasmid == "218" & host == "PHO2"), !plasmid %in% c("188", "194", NA)) %>% 
  nest(data = c(date, BL1.H, YL2.H), .by = c(plasmid, host))

day.var.gfp <- tmp %>% 
  mutate(model = map(data, function(df) lm(BL1.H ~ date, data = df)),
         tidied = map(model, broom::tidy)) %>% 
  unnest(tidied) %>% 
  filter(term != "(Intercept)") %>% 
  mutate(p.adj = p.adjust(p.value, method = "BH")) %>% 
  select(-data, -model) %>% 
  filter(p.adj < 0.10) %>% 
  arrange(plasmid, host)

day.var.rfp <- tmp %>% 
  mutate(model = map(data, function(df) lm(YL2.H ~ date, data = df)),
         tidied = map(model, broom::tidy)) %>% 
  unnest(tidied) %>% 
  filter(term != "(Intercept)") %>% 
  mutate(p.adj = p.adjust(p.value, method = "BH")) %>% 
  select(-data, -model) %>% 
  filter(p.adj < 0.10) %>% 
  arrange(plasmid, host)
```

```{r}
# extract ximera names
refs <- c("188","194")
# make a test set
day.var.gfp.list <- unique(day.var.gfp$plasmid)
day.var.rfp.list <- unique(day.var.rfp$plasmid)
```

High day-to-day GFP variance: `r day.var.gfp.list`
High day-to-day RFP variance: `r day.var.rfp.list`

Plotting components for chimeras with high day-to-day variance in Pho4-mNeon
```{r}
p <- my_plot_ratio(c(refs,day.var.gfp.list)) + 
    geom_point(data = function(x) subset(x, !symbol %in% c("CCCCC", "SSSSS")),
               aes(group = host, color = date), size = 1, shape = 3, alpha = 0.9,
               position = position_jitterdodge(dodge.width = 0.5, jitter.width = 0.1)) +
    scale_color_manual(values = date.colors, guide = "none")
p
```
> Watch out for CSCscC, SCCsS, SCCsS

Plotting components for chimeras with high day-to-day variance in _PHO5pr_-mCherry
```{r}
p <- my_plot_ratio(c(refs,day.var.rfp.list)) + 
    geom_point(data = function(x) subset(x, !symbol %in% c("CCCCC", "SSSSS")),
               aes(group = host, color = date), size = 1, shape = 3, alpha = 0.9,
               position = position_dodge(width = 0.5)) +
    scale_color_manual(values = date.colors, guide = "none")
p
```
> most of the day-to-day variance are canceled out after RFP/GFP normalization

# All chimera, scatter plot
Design the plot

```{r}
my_scatter_plot <- function(pattern){
  selection = my_data_select(pattern = pattern)
  scatter.colors = c("ScPho4" = "forestgreen", "CgPho4" = "blue3", "cyan2", "other" = "gray20")
  names(scatter.colors)[3] = pattern
  p <- ximera %>% 
    mutate(A_PHO2 = signif(A_PHO2, digits = 2),
           A_pho2 = signif(A_pho2, digits = 2),
           group = case_when(
             symbol == "CCCCC" ~ "CgPho4",
             symbol == "SSSSS" ~ "ScPho4",
             plasmid %in% selection ~ pattern,
             .default = "other"
           ),
           group = fct_relevel(group, names(scatter.colors))) %>% 
    ggplot(aes(x = A_PHO2, y = A_pho2, label = symbol)) + 
    geom_point(aes(color = group), size = 2.5) + 
    scale_color_manual("Pho4 type", values = scatter.colors) +
    geom_abline(slope = 1) +
    theme_gray(base_size = 14)
  return(p)
}
```

this function is the same as above, but is used to plot region 4 effects alone, and doesn't take any input
```{r}
my_scatter_plot_fix <- function(){
  # this function is the same as above, but is used to plot region 4
  # effects alone, and doesn't take any input
  s1 = my_data_select(pattern = "XXXCCX")
  s2 = my_data_select(pattern = "XXXSSX")
  scatter.colors = c("ScPho4" = "forestgreen", "CgPho4" = "blue3", 
                     "P2ID:Cg" = "cyan2", "P2ID:Sc" = "palegreen",
                     "P2ID:mixed" = "gray20")
  p <- ximera %>% 
    mutate(A_PHO2 = signif(A_PHO2, digits = 2),
           A_pho2 = signif(A_pho2, digits = 2),
           group = case_when(
             symbol == "CCCCC" ~ "CgPho4",
             symbol == "SSSSS" ~ "ScPho4",
             plasmid %in% s1 ~ "P2ID:Cg",
             plasmid %in% s2 ~ "P2ID:Sc",
             .default = "P2ID:mixed"
           ),
           group = fct_relevel(group, names(scatter.colors))) %>% 
    ggplot(aes(x = A_PHO2, y = A_pho2, label = symbol)) + 
    geom_abline(slope = 1) +
    geom_point(aes(color = group), size = 2.5) + 
    scale_color_manual(NULL, values = scatter.colors) +
    labs(x = bquote(A[PHO2]), y = bquote(A[pho2])) +
    theme_minimal(base_size = 16) +
    theme(legend.text = element_text(size = rel(0.75)))
  return(p)
}
```

this function is the same as my_scatter_plot_fix except that it plots all the chimeras without coloring them differently. for figure 5
```{r}
my_scatter_plot_all <- function(){
  # this function is the same as my_scatter_plot_fix except that it plots all the chimeras
  # without coloring them differently. for figure 5
  s1 = my_data_select(pattern = "XXXCCX")
  s2 = my_data_select(pattern = "XXXSSX")
  scatter.colors = c("ScPho4" = "forestgreen", "CgPho4" = "blue3", 
                     "P2ID:Cg" = "gray20", "P2ID:Sc" = "gray20",
                     "P2ID:mixed" = "gray20")
  scatter.size = c("ScPho4" = 3.5, "CgPho4" = 3.5,
                   "P2ID:Cg" = 2.5, "P2ID:Sc" = 2.5, "P2ID:mixed" = 2.5)
  p <- ximera %>% 
    mutate(A_PHO2 = signif(A_PHO2, digits = 2),
           A_pho2 = signif(A_pho2, digits = 2),
           group = case_when(
             symbol == "CCCCC" ~ "CgPho4",
             symbol == "SSSSS" ~ "ScPho4",
             plasmid %in% s1 ~ "P2ID:Cg",
             plasmid %in% s2 ~ "P2ID:Sc",
             .default = "P2ID:mixed"
           ),
           group = fct_relevel(group, names(scatter.colors))) %>% 
    ggplot(aes(x = A_PHO2, y = A_pho2, label = symbol)) + 
    geom_abline(slope = 1) +
    geom_point(aes(color = group, size = group)) + 
    scale_color_manual(NULL, values = scatter.colors) +
    scale_size_manual(values = scatter.size, guide = "none") +
    labs(x = bquote(A[PHO2]), y = bquote(A[pho2])) +
    theme_cowplot() + panel_border(color = "gray30", size = 1.2) +
    theme(legend.text = element_text(size = rel(0.8)),
          legend.position = "none",
          axis.title = element_text(face = 2, size = rel(1.2)),
          axis.line = element_blank())

  return(p)
}
```

Plot all chimeras, for Fig. 5
```{r}
p <- my_scatter_plot_all()
ggsave(filename = "../img/20241121-all-chimera-scatter.png",
       plot = p, width = 4.5, height = 4, dpi = 300)
ggplotly(p + labs(x = "A<sub>PHO2</sub>", y = "A<sub>pho2</sub>") +
           theme_gray(base_size = 16) +
           theme(legend.text = element_markdown()), 
         tooltip = c("label", "x", "y"))
```
```{r}
p <- my_scatter_plot_fix()
ggsave(filename = "../img/20231220-all-chimera-scatter-color-by-P2ID.png",
       plot = p, width = 6, height = 4, dpi = 300)
ggplotly(p + labs(x = "A<sub>PHO2</sub>", y = "A<sub>pho2</sub>") +
           theme_gray(base_size = 16) +
           theme(legend.text = element_markdown()), 
         tooltip = c("label", "x", "y"))
```

# ANOVA
```{r}
split <- c(1,1,1,1,1); names(split) <- paste0("P", 1:5)
tmp <- ximera %>% 
  filter(set == "M", group != "n.f.") %>% 
  separate_wider_position(symbol, split) %>% 
  mutate(across(P1:P5, ~factor(.x, levels = c("S", "C"))))
lm <- lm(A_pho2 ~ (P1+P2+P3+P4+P5), data = tmp)
summary(lm)
```
The main effects were calculated by averaging over all chimeras with CgPho4 region at the respective position. I'd like to break them down by backgrounds. For example, for region 3, I'd like to see the pairwise comparisons between CCCSS and CCSSS, where only region 3 differs. The steps are

1. select the region to be compared. split the symbol into two parts - the genotype of the focal region and the rest
2. group by the second part (rest) and calculate the differential

```{r}
my_calc_region_effect <- function(region, variable){
  # this function takes the name of a variable of interest
  # x specifies the foreground region, which will be examined for its effect on
  # the variable of interest.
  # it then transforms the ximera data frame to preserve only the variable of
  # interest, pivots it wider after grouping by the background composition.
  
  # prepare the data by mutating the symbol column into fg and bg
  valid.var <- c("A_PHO2", "A_pho2", "s_PHO2", "spho2", "boost")
  if(!variable %in% valid.var)
    stop(paste0("Please specify one of the valid variable names:", 
                paste(valid.var, collapse = ", ")))
  tmp <- ximera %>% 
    filter(set == "M") %>% 
    select(plasmid, symbol, var = {{ variable }}) %>% 
    mutate(fg = str_sub(symbol, region, region) %>% toupper(),
           bg = symbol %>% toupper())
  # replace the foreground region with X for grouping
  str_sub(tmp$bg, region, region) <- "X"
  # reorganize the tibble for easier handling, optional
  tmp <- relocate(tmp, fg, bg, .before = symbol) %>% select(-symbol)
  # pivot the data into a wide format such that for each background, there
  # are two values for the variable of interest, one from the chimera with 
  # CgPho4's version in the foreground and another with ScPho4's version
  tmp <- tmp %>% 
    select(plasmid, fg, bg, var) %>% 
    pivot_wider(id_cols = bg, names_from = "fg", 
                values_from = c(plasmid, var)) %>% 
    unite(plasmid, starts_with("plasmid")) %>%
    mutate(label = paste(bg, plasmid, sep = "\n"))
  return(tmp)
}

my_plot_region_effect_onevar <- function(region, variable){
  # this function uses `my_calc_region_effect` output as the data
  # and makes a xy scatter plot, where x shows the value of the variable of 
  # interest with CgPho4 in the focal region, and y for the ScPho4 version
  tmp <- my_calc_region_effect(region, variable)
  p <- ggplot(tmp, aes(x = var_C, y = var_S, label = label)) +
    geom_point(size = 2.5) + 
    geom_abline(slope = 1) +
    xlab(paste0("Region ", region, " from CgPho4")) +
    ylab(paste0("Region ", region, " from ScPho4")) +
    xlim(0, NA) + ylim(0, NA) +
    ggtitle(paste0("Effect on ", variable)) +
    theme_gray(base_size = 16) +
    theme(plot.title = element_text(hjust = 0.5))
  return(p)
}
```

```
x = 5
p1 <- my_plot_region_effect_onevar(x, "A_PHO2")
p2 <- my_plot_region_effect_onevar(x, "A_pho2")
subplot(p1, p2, margin = 0.05) %>% 
  layout(title = paste("Region", x, "swap effect on A_PHO2 and A_pho2", sep = " "),
         xaxis = list(title = paste0("Region ", x, " from CgPho4")),
         yaxis = list(title = paste0("Region ", x, " from ScPho4")) )
```

Here, I'd like to take what I build above and create a new tibble, in which each row is a different background (makeup of the chimera except for the focal region). The value columns are:

1. dA_PHO2 = A_PHO2_Cg - A_PHO2_Sc
2. dA_pho2 = A_pho2_Cg - A_pho2_Sc
3. A_PHO2_Sc = A_PHO2_Sc

The goal is to plot dA_PHO2 and dA_pho2 side-by-side for each background.
```{r}
my_comp_region_effect <- function(region){
  # this function uses my_calc_region_effect to get the value for the variable of interest
  # with either Cg or Sc version in the focal region, separately for each background composition
  # it does so for two variables, A_PHO2 and A_pho2, then calculate dA_PHO2, dA_pho2, and
  # combine them
  PHO2 = my_calc_region_effect(region, "A_PHO2") %>% 
    mutate(dA_PHO2 = var_C - var_S,
           # mean A_PHO2
           M_PHO2 = (var_S + var_C)/2,
           NF = ifelse(M_PHO2 <=3.5, TRUE, FALSE)) %>% 
    select(-var_S, -var_C)
  
  pho2 = my_calc_region_effect(region, "A_pho2") %>% 
    mutate(dA_pho2 = var_C - var_S, 
           M_pho2 = (var_S + var_C)/2) %>% 
    select(-var_S, -var_C)
  
  dat <- full_join(PHO2, pho2, by = c("bg", "plasmid", "label")) %>% 
    select(bg, plasmid, dA_PHO2, dA_pho2, M_PHO2, M_pho2, NF)
  
  return(dat)
}

my_plot_region_effect_twovar_line <- function(region, highlight = "none"){
  # this function uses my_comp_region_effect to generate the data
  # and plot the difference in A_PHO2 and A_pho2 between the CgPho4 vs ScPho4
  # in the focal region
  dat <- my_comp_region_effect(region) %>% 
    pivot_longer(cols = c(dA_PHO2, dA_pho2), 
                 names_to = "host", values_to = "diff") %>% 
    mutate(host = fct_recode(host, `PHO2` = "dA_PHO2", `pho2∆` = "dA_pho2"),
           host = fct_relevel(host, "PHO2"))
  if(highlight != "none" & highlight != region){
    hl = as.numeric(highlight)
    dat <- mutate(dat, grp = str_sub(bg, hl, hl) %>% toupper(),
                  grp = fct_recode(grp, CgPho4 = "C", ScPho4 = "S"))
  }else{
    dat <- mutate(dat, grp = ifelse(NF, "n.f.", "others"))
  }
  # specify legend title
  legend.title = ""
  if(highlight != "none" & highlight != region){
    hl = as.numeric(highlight)
    dat <- mutate(dat, grp = str_sub(bg, hl, hl) %>% toupper(),
                  grp = fct_recode(grp, CgPho4 = "C", ScPho4 = "S"))
    legend.title = paste("Region", highlight, sep = " ")
  }else{
    dat <- mutate(dat, grp = ifelse(NF, "no", "yes"))
    legend.title = "Functional"
  }
  # specify arrow annotation
  arrow.x = 0.7
  arrow.y = (max(dat$diff) - min(dat$diff)) / 5 
  p <- dat %>% 
    ggplot(aes(x = host, y = diff, label = bg)) +
    geom_point(aes(color = grp), size = 2, alpha = 0.8,
               position = position_jitter(0.05)) + 
    geom_line(aes(group = bg), linewidth = 0.2, alpha = 0.8) +
    geom_segment(aes(x = arrow.x, xend = arrow.x, y = -arrow.y, yend = arrow.y),
                 arrow = arrow(length = unit(0.03, "npc"), ends = "both"), 
                 color = "gray60", lwd = 1, alpha = 0.5) +
    geom_segment(aes(x = arrow.x - 0.05, xend = arrow.x + 0.05, y = 0, yend = 0),
                 lwd = 2, color = "gray60") +
    annotate("text", x = arrow.x - 0.1, y = 5, label = "CgPho4++", 
             angle = '90', color = "gray30") +
    annotate("text", x = arrow.x + 0.1, y = -5, label = "ScPho4++", 
             angle = '270', color = "gray30") +
    scale_color_manual(legend.title, values = c("orange", "gray20")) +
    ylab("Region swap effect (Cg-Sc)") +
    theme_bw(base_size = 18) + 
    theme(
      axis.title.x = element_blank(),
      axis.title.y = element_text(size = rel(0.9)),
      legend.text = element_text(size = rel(0.8)),
      legend.title = element_text(size = rel(0.9)),
    )
  return(p)
}

my_plot_region_effect_twovar_line("4", "5")# %>% ggplotly()
ggsave("../img/20231221-region-swap-effect-4-on-5.png", width = 6, height = 4, dpi = 150)
```

```{r}
my_plot_region_effect_twovar_line("5", "4")# %>% ggplotly()
ggsave("../img/20231224-region-swap-effect-5-on-4.png", width = 6, height = 4, dpi = 200)
```

```{r}
my_plot_region_effect_twovar_line_par <- function(regions, highlight = "none"){
  # this function uses my_comp_region_effect to generate the data
  # and plot the difference in A_PHO2 and A_pho2 between the CgPho4 vs ScPho4
  # in the focal region
  dat <- map_dfr(regions, \(region) my_comp_region_effect(region), .id = "region") %>% 
    pivot_longer(cols = c(dA_PHO2, dA_pho2), 
                 names_to = "host", values_to = "diff") %>% 
    mutate(host = fct_recode(host, `PHO2` = "dA_PHO2", `pho2∆` = "dA_pho2"),
           host = fct_relevel(host, "PHO2"))
  # specify legend title
  legend.title = ""
  if(highlight != "none" & !highlight %in% regions){
    hl = as.numeric(highlight)
    dat <- mutate(dat, grp = str_sub(bg, hl, hl) %>% toupper(),
                  grp = fct_recode(grp, CgPho4 = "C", ScPho4 = "S"))
    legend.title = paste("Region", highlight, "from", sep = " ")
  }else{
    dat <- mutate(dat, grp = ifelse(NF, "no", "yes"))
    legend.title = "Functional"
  }
  # specify arrow annotation
  arrow.x = 0.7
  arrow.y = (max(dat$diff) - min(dat$diff)) / 5 
  p <- dat %>% 
    ggplot(aes(x = host, y = diff, label = bg)) +
    geom_point(aes(color = grp), size = 2, alpha = 0.8,
               position = position_jitter(0.1)) + 
    geom_line(aes(group = bg), linewidth = 0.2, alpha = 0.8) +
    facet_grid(grp ~ region, labeller = labeller(
      grp = c(CgPho4 = "P2ID:CgPho4", ScPho4 = "P2ID:ScPho4"),
      region = label_both
    )) +
    scale_color_manual("P2ID:", values = c("orange", "gray20")) +
    ylab("Region swap effect (Cg-Sc)") +
    theme_bw(base_size = 18) + 
    theme(
      axis.title.x = element_blank(),
      axis.title.y = element_text(size = rel(0.9)),
      legend.text = element_text(size = rel(0.8)),
      legend.title = element_text(size = rel(0.9)),
    )
  return(p)
}
my_plot_region_effect_twovar_line_par(c(1,2,3), "4")
ggsave("../img/20231224-region-swap-effect-1to3-on-4.png")
```

```{r}
my_plot_region_effect_twovar_side <- function(region){
  # this function uses my_comp_region_effect to generate the data
  # and plot the difference in A_PHO2 and A_pho2 between the CgPho4 vs ScPho4
  # in the focal region
  dat <- my_comp_region_effect(region) %>% 
    pivot_longer(cols = c(dA_PHO2, dA_pho2), 
                 names_to = "host", values_to = "diff") %>% 
    mutate(host = fct_recode(host, `PHO2` = "dA_PHO2", `pho2∆` = "dA_pho2"),
           host = fct_relevel(host, "PHO2"))
  p <- dat %>% 
    ggplot(aes(x = bg, y = diff, group = host)) +
    geom_col(aes(fill = host), position = position_dodge(0.9)) +
    scale_fill_manual(values = host.colors) +
    ylab("Region swap diff (Cg vs Sc)") +
    theme_cowplot(font_size = 20) + 
    panel_border(color = "gray30") +
    background_grid(major = "y", minor = "none") +
    theme(axis.text.x = element_text(angle = 45, hjust = 1, family = "courier"),
          axis.title.x = element_blank(),
          legend.position = "top")
  return(p)
}
```

# triangle heatmap
First, write a function to generate the data for plotting. If we are going to use ggplot, we need a tibble to store the data, something in the following form

| plasmid | symbol | RegionA | RegionB | A_PHO2 | A_pho2 | s_PHO2 | boost | perc_pho2 |
|:------- |:------ |:------- |:------- |:------ |:------ |:------ |:----- |:--------- |
| 209     | CCSCC  | 3       | 3       | 8.25   | 7.82   | 0.468  | 1.06  | 0.94      |

If we are ok with using non ggplot - heatmaps are not ggplot's strength anyways - we can just build a matrix.

Note that this way of summarizing the data has many limitaitons: 1) it requires specifying the reference, either CCCCC or SSSSS. Everything is measured against that; 2) it only shows pairwise (two region) interactions. This turns out to be fine with five regions, since every chimera can be expressed as either a 0, 1 or 2 region swap from one of the two reference genotypes. With 6 or more regions, higher level (3 or more region) interactions cannot be visualized this way. Because of this, we will focus on just the main set for this analysis.

To build the matrix, we need to first identify the chimeras that belong to the set. For that, we will use the "main" set, with the five region split, for the moment at least. The function will first determine which reference to use. If we use SSSSS as the reference, for example, we will assign 0 to the reference. All other chimeras with 1 or 2 regions from Cg will be used to fill an upper triangular matrix, using one of the values of interest, e.g., A_PHO2.
```{r}
my_upper_triangular_mat <- function(alt = "C", var = "A_PHO2"){
  # given the alternative allele (C/S) and a variable of interest, e.g., A_PHO2,
  # output an upper triangular matrix containing the values from the variable 
  # of interest, with the row and col numbers based on the first and second
  # positions containing the alternative allele. If all positions contain the 
  # reference allele, the value is subtracted from all values in the matrix
  # when just one position is the alternative allele, the value in the diagonal
  # is set. when there are more than 2 regions containing the alternative allele
  # skip.
  out_mat <- matrix(NA, nrow = 5, ncol = 5)
  ref_val <- NA
  dat <- filter(ximera, set == "M") %>% 
    mutate(S = as.character(symbol) %>% toupper())
  for(i in seq(1, nrow(dat))){
    symbol = dat[i, "S"]
    # determine which positions contain the alternative allele
    p = str_locate_all(symbol, alt)[[1]][,"start"]
    l = length(p)   # how many positions contain the alt allele
    v = dat[[var]][i] # retrieve the value of the variable
    if(l == 0)
      ref_val = v
    else if(l == 1)
      out_mat[p, p] = v
    else if(l == 2)
      out_mat[p[1], p[2]] = v
  }
  out_mat = out_mat - ref_val
  return(out_mat)
}
```
```{r}
my_combined_triangular_mat <- function(alt = "C"){
  # given the alternative allele (C/S), output a matrix containing the values
  # for both with and without Pho2, arranged in two complementary triagular
  # matrices, with the row and col numbers based on the first and second
  # positions containing the alternative allele. If all positions contain the 
  # reference allele, the value is subtracted from all values in the matrix
  # when just one position is the alternative allele, the value in the diagonal
  # is set. when there are more than 2 regions containing the alternative allele
  # skip.
  out_mat <- matrix(NA, nrow = 6, ncol = 6)
  upper <- cbind(NA, my_upper_triangular_mat(alt, var = "A_PHO2")) %>% 
    rbind(., NA)
  lower <- rbind(NA, t(my_upper_triangular_mat(alt, var = "A_pho2"))) %>% 
    cbind(., NA)
  out_mat = ifelse(is.na(upper), lower, upper)
  return(out_mat)
}
```

```{r}
my_plot_triangle_heatmap <- function(alt, var){
  # this function takes the output of the function above and makes a heatmap
  # using pheatmap function, then rotates it using grid graphics
  # thanks to https://bookdown.org/rdpeng/RProgDA/the-grid-package.html#grid-graphics-coordinate-systems
  # adding title based on https://davetang.github.io/muse/pheatmap.html
  
  # construct title of plot
  ref = ifelse(alt == "C", "ScPho4", "CgPho4")
  bg = ifelse(var == "A_PHO2", "with PHO2", "w/o pho2")
  my_title <- paste("Epistasis between regions on", ref, "background", bg)
  test <- my_upper_triangular_mat(alt = alt, var = var)
  paletteLength = 50
  myColors <- colorRampPalette(c("steelblue4", "gray90", "red"))(paletteLength)
  rng <- max(abs(test), na.rm = TRUE)
  myBreaks <- c(seq(-rng, 0, length.out=ceiling(paletteLength/2) + 1), 
                seq(rng/paletteLength, rng,
                    length.out=floor(paletteLength/2)))
  p <- pheatmap::pheatmap(test, color = myColors, breaks = myBreaks,
                          border_color = NA, na_col = NA, silent = TRUE,
                          cluster_cols = FALSE, cluster_rows = FALSE)
  vp <- viewport(x = 0.5, y = 0.25,
                 width = unit(4.5, "in"), height = unit(4.5, "in"), angle = 47) 
  grid.newpage()
  pushViewport(vp)
  grid.draw(p$gtable)
  popViewport()
  grid.text(label = my_title, x = 0.5, y = 0.95, gp = gpar(fontsize = 16, fontface = "bold"))
  return(p)
}
```
```{r}
my_plot_combined_triangle_heatmap <- function(alt){
  # this function takes the output of the function my_combined_triangular_mat()
  # using pheatmap function, then rotates it using grid graphics
  # thanks to https://bookdown.org/rdpeng/RProgDA/the-grid-package.html#grid-graphics-coordinate-systems
  # adding title based on https://davetang.github.io/muse/pheatmap.html
  
  # construct title of plot
  ref = ifelse(alt == "C", "ScPho4", "CgPho4")
  my_title <- paste("Epistasis between regions on", ref, "background")
  test <- my_combined_triangular_mat(alt = alt)
  paletteLength = 50
  myColors <- colorRampPalette(c("steelblue4", "gray90", "red"))(paletteLength)
  rng <- max(abs(test), na.rm = TRUE)
  myBreaks <- c(seq(-rng, 0, length.out=ceiling(paletteLength/2) + 1), 
                seq(rng/paletteLength, rng,
                    length.out=floor(paletteLength/2)))
  p <- pheatmap::pheatmap(test, color = myColors, breaks = myBreaks,
                          border_color = NA, na_col = NA, silent = TRUE,
                          cluster_cols = FALSE, cluster_rows = FALSE)
  vp <- viewport(x = 0.5, y = 0.45,
                 width = unit(3, "in"), height = unit(2.8, "in"), angle = 47) 
  grid.newpage()
  pushViewport(vp)
  grid.draw(p$gtable)
  popViewport()
  grid.text(label = my_title, x = 0.5, y = 0.95, 
            gp = gpar(fontsize = 16, fontface = "bold"))
  grid.text(label = "With Pho2", x = 0.1, y = 0.65, just = c("left", "top"),
            gp = gpar(fontsize = 14, fontface = "bold"))
  grid.text(label = "Without pho2", x = 0.1, y = 0.25, just = c("left", "top"), 
            gp = gpar(fontsize = 14, fontface = "bold"))
  return(p)
}
```

```{r}
p1 <- my_plot_combined_triangle_heatmap("C")
p2 <- my_plot_combined_triangle_heatmap("S")
```

```{r}
p1 <- my_plot_triangle_heatmap("C", "A_PHO2")
p2 <- my_plot_triangle_heatmap("C", "A_pho2")
p3 <- my_plot_triangle_heatmap("S", "A_PHO2")
p4 <- my_plot_triangle_heatmap("S", "A_pho2")
```

